Internet Traffic Prediction by strongWstrong-Boost Classification and.pdfVIP

Internet Traffic Prediction by W-Boost: Classification and Regression∗ 1 2 1 1 Hanghang Tong , Chongrong Li , Jingrui He , and Yang Chen 1 Department of Automation, Tsinghua University, Beijing 100084, China {walkstar98,hejingrui98}@mails.tsinghua.edu.cn 2 Network Research Center of Tsinghua University, Beijing 100084, China licr@cernet.edu.cn Abstract. Internet traffic prediction plays a fundamental role in network de- sign, management, control, and optimization. The self-similar and non-linear nature of network traffic makes highly accurate prediction difficult. In this pa- per, we proposed a new boosting scheme, namely W-Boost, for traffic predic- tion from two perspectives: classification and regression. To capture the non- linearity of the traffic while introducing low complexity into the algorithm, ëstumpí and piece-wise-constant function are adopted as weak learners for clas- sification and regression, respectively. Furthermore, a new weight update scheme is proposed to take the advantage of the correlation information within the traffic for both models. Experimental results on real network traffic which exhibits both self-similarity and non-linearity demonstrate the effectiveness of the proposed W-Boost. 1 Introduction Internet traffic prediction plays a fundamental role in network design, management, control, and optimization [12]. Essentially, the statistics of network traffic itself de- termines the predictability of network traffic [2], [12]. Two of the most important discoveries of the statistics of Interne